Cheatsheet - Programming for Data Science (Fall 2025)

Patterns and examples of how to use common tips in class

How to use brackets¶

symbol	use
`[val]`	indexing item val from an object; `val` is int for iterables, or any for mapping
`[val : val2]`	slicing elemtns val to val2-1 from a listlike object
`[ item1,item2 ]`	creating a list consisting of `item1` and `item2`
`(param)`	function calls
`(item1,item2)`	defining a tuple of `item1` and `item2`
`{item1,item2}`	defining a set of `item1` and `item2`
`{key:val1, key2: val2}`	defining a dictionary where key1 indexes to val2

Axes¶

import pandas as pd

First build a small dataset that’s just enough to display

data = [[1,0],[5,4],[1,4]]
df = pd.DataFrame(data = data,
  columns = ['A','B'])

df

This data frame is originally 3 rows, 2 columns. So summing across rows will give us a Series of length 3 (one per row) and long columns will give length 2, (one per column). Setting up our toy dataset to not be a square was important so that we can use it to check which way is which.

df.sum(axis=0)

A    7
B    8
dtype: int64

df.sum(axis=1)

0    1
1    9
2    5
dtype: int64

df.apply(sum,axis=0)

A    7
B    8
dtype: int64

df.apply(sum,axis=1)

0    1
1    9
2    5
dtype: int64

Indexing¶

df['A'][1]

np.int64(5)

df.iloc[0][1]

/tmp/ipykernel_2503/2309508539.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  df.iloc[0][1]

np.int64(0)

Markdown Headings¶

To create a heading in a notebook, make a a line that starts with a #

# This is a main title
## This is a sub heading
### sub sub heading
## sub heading

Resources

References on Python

Resources

Modeling Preliminaries